• Title/Summary/Keyword: adaptive neural network control

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Maximum Torque Control of IPMSM with Adaptive Learning Fuzzy-Neural Network (적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Lee, Jung-Ho;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2006.05a
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    • pp.309-314
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    • 2006
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

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Fuzzy Rules Optimizing by Neural Network-based Adaptive Fuzzy Control

  • K, K.-Wong;Akio, Katuki
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.96.2-96
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    • 2001
  • This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

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The Speed Control and Estimation of IPMSM using Adaptive FNN and ANN

  • Lee, Hong-Gyun;Lee, Jung-Chul;Nam, Su-Myeong;Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1478-1481
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    • 2005
  • As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

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Maximum Torque Control of IPMSM with Adoptive Leaning Fuzzy-Neural Network (적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어)

  • Chung, Dong-Hwa;Ko, Jae-Sub;Choi, Jung-Sik
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.21 no.5
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    • pp.32-43
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    • 2007
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

Adaptive FNN Controller for High Performance Control of Induction Motor Drive (유도전동기 드라이브의 고성능 제어를 위한 적응 FNN 제어기)

  • 이정철;이홍균;정동화
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.53 no.9
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    • pp.569-575
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    • 2004
  • This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

A Robust Nonlinear Control Using the Neural Network Model on System Uncertainty (시스템의 불확실성에 대한 신경망 모델을 통한 강인한 비선형 제어)

  • 이수영;정명진
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.43 no.5
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    • pp.838-847
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    • 1994
  • Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

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Composite adaptive neural network controller for nonlinear systems (비선형 시스템제어를 위한 복합적응 신경회로망)

  • 김효규;오세영;김성권
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.14-19
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    • 1993
  • In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

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High Performance of Induction Motor Drive with HAl Controller (HAI 제어기에 의한 유도전동기 드라이브의 고성능 제어)

  • Nam, Su-Myeong;Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.570-572
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    • 2005
  • This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

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Adaptive Backstepping Control of Induction Motors Using Neural Network (신경회로망을 이용한 유도전동기의 적응 백스테핑 제어)

  • Lee, Eun-Wook;Yang, Hai-Won
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.452-455
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    • 2003
  • Based on a field-oriented model of induction motor, adaptive backstepping approach using neural network(RBFN) is proposed for the control of induction motor in this paper. In order to achieve the speed regulation with the consideration of avoiding singularity and improving power efficiency, rotor angular speed and flux amplitude tracking objectives are formulated. rotor resistance uncertainty is compensated by adaptive backstepping and mechanical lumped uncertainty such as load torque disturbance, inertia moment, friction by RBFN. Simulation is provided to verify the effectiveness of the proposed approach.

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Control Method of an Unknown Nonlinear System Using Dynamical Neural Network (동적 신경회로망을 이용한 미지의 비선형 시스템 제어 방식)

  • 정경권;임중규;엄기환
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.3
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    • pp.487-492
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    • 2002
  • In this paper, we proposed a control method of an unknown nonlinear system using a dynamical neural network. The proposed method is composed of neural network of state space model type, performs for a unknown nonlinear system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed method, we simulated one-link manipulator. The simulation results showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.